code: def _load_model_from_path(path: str, model_config=None):
      model_load_fn = model_config.get(_MODEL_LOAD_KEY)
      if model_load_fn == _RUNNABLE_LOAD_KEY:
          return _load_runnables(path, model_config)
      if model_load_fn == _BASE_LOAD_KEY:
          return _load_base_lcs(path, model_config)
      if model_load_fn == _CONFIG_LOAD_KEY:
          return _load_model_from_config(path, model_config)
      raise MlflowException(
          f"Unsupported model load key {model_load_fn}"
      )
  
file_path: mlflow/mlflow/langchain/runnables.py
imports: import os
  from pathlib import Path
  from typing import Union
  import cloudpickle
  import yaml
  from mlflow.exceptions import MlflowException
  from mlflow.langchain.utils import _BASE_LOAD_KEY, _CONFIG_LOAD_KEY, _MODEL_DATA_FOLDER_NAME, _MODEL_DATA_KEY, _MODEL_DATA_PKL_FILE_NAME, _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY, _MODEL_TYPE_KEY, _RUNNABLE_LOAD_KEY, _UNSUPPORTED_MODEL_ERROR_MESSAGE, _load_base_lcs, _load_from_json, _load_from_pickle, _load_from_yaml, _save_base_lcs, _validate_and_wrap_lc_model, base_lc_types, custom_type_to_loader_dict, lc_runnable_branch_type, lc_runnable_with_steps_types, lc_runnables_types, picklable_runnable_types
label_desc: Use image node_id 2 for calling a global function with example usage: _load_model_from_path(path, model_config) and returns: _load_runnables, _load_base_lcs, _load_model_from_config